Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells10104
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

NUM10
CAT8

Warnings

property_type has constant value "10000" Constant
country has constant value "10000" Constant
city has constant value "10000" Constant
current_zones has a high cardinality: 137 distinct values High cardinality
zone has a high cardinality: 132 distinct values High cardinality
current_zones has 545 (5.4%) missing values Missing
zone has 545 (5.4%) missing values Missing
closed_price has 9014 (90.1%) missing values Missing
interior_area is highly skewed (γ1 = 35.28555918) Skewed
gros_area is highly skewed (γ1 = 36.8104142) Skewed
year_of_construction is highly skewed (γ1 = 80.05373257) Skewed
propertiesid has unique values Unique
interior_area has 953 (9.5%) zeros Zeros
gros_area has 1102 (11.0%) zeros Zeros
bedrooms has 424 (4.2%) zeros Zeros
bathrooms has 545 (5.5%) zeros Zeros
other_rooms has 9005 (90.0%) zeros Zeros
year_of_construction has 8029 (80.3%) zeros Zeros
year_of_renovation has 9960 (99.6%) zeros Zeros

Reproduction

Analysis started2021-05-25 17:13:57.922380
Analysis finished2021-05-25 17:14:19.609878
Duration21.69 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

propertiesid
Real number (ℝ≥0)

UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402203.9997
Minimum86536
Maximum948301
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2021-05-25T19:14:19.719793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum86536
5-th percentile88214.95
Q195806.75
median399785
Q3676641.75
95-th percentile905132.1
Maximum948301
Range861765
Interquartile range (IQR)580835

Descriptive statistics

Standard deviation303062.6713
Coefficient of variation (CV)0.7535048671
Kurtosis-1.401180218
Mean402203.9997
Median Absolute Deviation (MAD)303153.5
Skewness0.3223707184
Sum4022039997
Variance9.184698275e+10
MonotocityStrictly decreasing
2021-05-25T19:14:19.917345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1023981< 0.1%
 
1016911< 0.1%
 
3781861< 0.1%
 
894171< 0.1%
 
912141< 0.1%
 
8748211< 0.1%
 
6853801< 0.1%
 
994351< 0.1%
 
4355181< 0.1%
 
8266811< 0.1%
 
Other values (9990)999099.9%
 
ValueCountFrequency (%) 
865361< 0.1%
 
865431< 0.1%
 
865451< 0.1%
 
865461< 0.1%
 
865471< 0.1%
 
ValueCountFrequency (%) 
9483011< 0.1%
 
9482751< 0.1%
 
9482191< 0.1%
 
9478611< 0.1%
 
9475961< 0.1%
 

property_type
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Apartment
10000 
ValueCountFrequency (%) 
Apartment10000100.0%
 
2021-05-25T19:14:20.109041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:14:20.238158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:20.314024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

property_status
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Used
5964 
New
3461 
Under Construction
 
460
Under construction
 
90
In project
 
10
Other values (4)
 
15
ValueCountFrequency (%) 
Used596459.6%
 
New346134.6%
 
Under Construction4604.6%
 
Under construction900.9%
 
In project100.1%
 
Remodelled60.1%
 
Refurbished50.1%
 
For refurbishment3< 0.1%
 
To demolish or rebuild1< 0.1%
 
2021-05-25T19:14:20.555662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-25T19:14:20.713639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:20.863832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length4
Mean length4.4427
Min length3

availability
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Withdrawn
6455 
Available
2071 
Sold
996 
In evaluation
 
266
WithDrawn
 
162
Other values (3)
 
50
ValueCountFrequency (%) 
Withdrawn645564.5%
 
Available207120.7%
 
Sold99610.0%
 
In evaluation2662.7%
 
WithDrawn1621.6%
 
Reserved340.3%
 
In negotiation80.1%
 
Rented80.1%
 
2021-05-25T19:14:21.039600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:14:21.173609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:21.305691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length9
Mean length8.6066
Min length4

country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Albania
10000 
ValueCountFrequency (%) 
Albania10000100.0%
 
2021-05-25T19:14:21.467449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:14:21.591834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:21.714626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

division
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Tirana
9998 
Berat
 
1
Budva
 
1
ValueCountFrequency (%) 
Tirana9998> 99.9%
 
Berat1< 0.1%
 
Budva1< 0.1%
 
2021-05-25T19:14:21.951934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-05-25T19:14:22.079963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:22.170155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.9998
Min length5

city
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Tirana
10000 
ValueCountFrequency (%) 
Tirana10000100.0%
 
2021-05-25T19:14:22.332800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:14:22.452821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:22.521751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

current_zones
Categorical

HIGH CARDINALITY
MISSING

Distinct137
Distinct (%)1.4%
Missing545
Missing (%)5.4%
Memory size78.1 KiB
Fresku
 
521
Komuna e Parisit
 
493
21 Dhjetori
 
468
Astiri
 
467
Don Bosco
 
373
Other values (132)
7133 
ValueCountFrequency (%) 
Fresku5215.2%
 
Komuna e Parisit4934.9%
 
21 Dhjetori4684.7%
 
Astiri4674.7%
 
Don Bosco3733.7%
 
Ali Demi3643.6%
 
Ish Blloku3333.3%
 
Liqeni i Thatë3293.3%
 
Rruga e Kavajës2572.6%
 
Yzberish2202.2%
 
Other values (127)563056.3%
 
(Missing)5455.5%
 
2021-05-25T19:14:22.715280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique21 ?
Unique (%)0.2%
2021-05-25T19:14:22.926591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length11
Mean length12.0128
Min length3

zone
Categorical

HIGH CARDINALITY
MISSING

Distinct132
Distinct (%)1.4%
Missing545
Missing (%)5.4%
Memory size78.1 KiB
Fresku
 
521
Komuna e Parisit
 
493
21 Dhjetori
 
468
Astiri
 
467
Don Bosco
 
373
Other values (127)
7133 
ValueCountFrequency (%) 
Fresku5215.2%
 
Komuna e Parisit4934.9%
 
21 Dhjetori4684.7%
 
Astiri4674.7%
 
Don Bosco3733.7%
 
Ali Demi3643.6%
 
Ish Blloku3333.3%
 
Liqeni i Thatë3293.3%
 
Kodra e Diellit Residence2682.7%
 
Rruga e Kavajës2572.6%
 
Other values (122)558255.8%
 
(Missing)5455.5%
 
2021-05-25T19:14:23.160686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19 ?
Unique (%)0.2%
2021-05-25T19:14:23.402659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length11
Mean length12.1176
Min length3

price
Real number (ℝ≥0)

Distinct1258
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105512.7118
Minimum0
Maximum3823648
Zeros40
Zeros (%)0.4%
Memory size78.1 KiB
2021-05-25T19:14:23.599908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41000
Q162825
median85000
Q3121743.75
95-th percentile235000
Maximum3823648
Range3823648
Interquartile range (IQR)58918.75

Descriptive statistics

Standard deviation94015.19219
Coefficient of variation (CV)0.8910319013
Kurtosis429.4628736
Mean105512.7118
Median Absolute Deviation (MAD)27000
Skewness14.09199882
Sum1055127118
Variance8838856363
MonotocityNot monotonic
2021-05-25T19:14:23.797412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
750002282.3%
 
650002062.1%
 
850002042.0%
 
800001791.8%
 
550001731.7%
 
600001681.7%
 
900001681.7%
 
1100001601.6%
 
700001581.6%
 
1000001461.5%
 
Other values (1248)821082.1%
 
ValueCountFrequency (%) 
0400.4%
 
13< 0.1%
 
77.31< 0.1%
 
861< 0.1%
 
1002< 0.1%
 
ValueCountFrequency (%) 
38236481< 0.1%
 
32000001< 0.1%
 
25000001< 0.1%
 
20000001< 0.1%
 
10500001< 0.1%
 

interior_area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct270
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.0103
Minimum0
Maximum5700
Zeros953
Zeros (%)9.5%
Memory size78.1 KiB
2021-05-25T19:14:24.001382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q163
median86
Q3106
95-th percentile147
Maximum5700
Range5700
Interquartile range (IQR)43

Descriptive statistics

Standard deviation102.636134
Coefficient of variation (CV)1.179586027
Kurtosis1654.606222
Mean87.0103
Median Absolute Deviation (MAD)21
Skewness35.28555918
Sum870103
Variance10534.17601
MonotocityNot monotonic
2021-05-25T19:14:24.192447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09539.5%
 
1001871.9%
 
901801.8%
 
951681.7%
 
941671.7%
 
801591.6%
 
751481.5%
 
601481.5%
 
851441.4%
 
841411.4%
 
Other values (260)760576.0%
 
ValueCountFrequency (%) 
09539.5%
 
131< 0.1%
 
154< 0.1%
 
171< 0.1%
 
201< 0.1%
 
ValueCountFrequency (%) 
57001< 0.1%
 
50001< 0.1%
 
37461< 0.1%
 
27771< 0.1%
 
22001< 0.1%
 

gros_area
Real number (ℝ)

SKEWED
ZEROS

Distinct303
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.9963
Minimum-2
Maximum7600
Zeros1102
Zeros (%)11.0%
Memory size78.1 KiB
2021-05-25T19:14:24.393264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q168
median94
Q3115
95-th percentile165
Maximum7600
Range7602
Interquartile range (IQR)47

Descriptive statistics

Standard deviation135.222345
Coefficient of variation (CV)1.423448545
Kurtosis1718.279534
Mean94.9963
Median Absolute Deviation (MAD)24
Skewness36.8104142
Sum949963
Variance18285.08259
MonotocityNot monotonic
2021-05-25T19:14:24.599511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0110211.0%
 
1002112.1%
 
1102032.0%
 
1051761.8%
 
751721.7%
 
1201651.7%
 
901491.5%
 
701481.5%
 
951431.4%
 
1151391.4%
 
Other values (293)739273.9%
 
ValueCountFrequency (%) 
-21< 0.1%
 
0110211.0%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
76001< 0.1%
 
64501< 0.1%
 
50001< 0.1%
 
37461< 0.1%
 
35001< 0.1%
 

bedrooms
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8157
Minimum0
Maximum21
Zeros424
Zeros (%)4.2%
Memory size78.1 KiB
2021-05-25T19:14:24.783580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8407164745
Coefficient of variation (CV)0.4630260916
Kurtosis38.02335821
Mean1.8157
Median Absolute Deviation (MAD)0
Skewness2.303387857
Sum18157
Variance0.7068041904
MonotocityNot monotonic
2021-05-25T19:14:24.941003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
2543154.3%
 
1268726.9%
 
3133613.4%
 
04244.2%
 
4930.9%
 
670.1%
 
570.1%
 
1160.1%
 
850.1%
 
72< 0.1%
 
Other values (2)2< 0.1%
 
ValueCountFrequency (%) 
04244.2%
 
1268726.9%
 
2543154.3%
 
3133613.4%
 
4930.9%
 
ValueCountFrequency (%) 
211< 0.1%
 
1160.1%
 
101< 0.1%
 
850.1%
 
72< 0.1%
 

bathrooms
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3635
Minimum0
Maximum7
Zeros545
Zeros (%)5.5%
Memory size78.1 KiB
2021-05-25T19:14:25.094561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6201662766
Coefficient of variation (CV)0.4548340862
Kurtosis2.556597982
Mean1.3635
Median Absolute Deviation (MAD)0
Skewness0.3820484973
Sum13635
Variance0.3846062106
MonotocityNot monotonic
2021-05-25T19:14:25.239490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
1543054.3%
 
2390939.1%
 
05455.5%
 
3940.9%
 
4140.1%
 
670.1%
 
71< 0.1%
 
ValueCountFrequency (%) 
05455.5%
 
1543054.3%
 
2390939.1%
 
3940.9%
 
4140.1%
 
ValueCountFrequency (%) 
71< 0.1%
 
670.1%
 
4140.1%
 
3940.9%
 
2390939.1%
 

other_rooms
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1226
Minimum0
Maximum6
Zeros9005
Zeros (%)90.0%
Memory size78.1 KiB
2021-05-25T19:14:25.416939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4190307849
Coefficient of variation (CV)3.417869371
Kurtosis31.61866298
Mean0.1226
Median Absolute Deviation (MAD)0
Skewness4.744567268
Sum1226
Variance0.1755867987
MonotocityNot monotonic
2021-05-25T19:14:25.560063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0900590.0%
 
18448.4%
 
2920.9%
 
3440.4%
 
4110.1%
 
62< 0.1%
 
52< 0.1%
 
ValueCountFrequency (%) 
0900590.0%
 
18448.4%
 
2920.9%
 
3440.4%
 
4110.1%
 
ValueCountFrequency (%) 
62< 0.1%
 
52< 0.1%
 
4110.1%
 
3440.4%
 
2920.9%
 

year_of_construction
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.2323
Minimum0
Maximum199636
Zeros8029
Zeros (%)80.3%
Memory size78.1 KiB
2021-05-25T19:14:25.737152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2020
Maximum199636
Range199636
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2146.456291
Coefficient of variation (CV)5.181769483
Kurtosis7423.197993
Mean414.2323
Median Absolute Deviation (MAD)0
Skewness80.05373257
Sum4142323
Variance4607274.61
MonotocityNot monotonic
2021-05-25T19:14:25.941349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0802980.3%
 
20213994.0%
 
20202662.7%
 
20101781.8%
 
20051011.0%
 
2019900.9%
 
2008710.7%
 
2012610.6%
 
2000610.6%
 
2015590.6%
 
Other values (52)6856.9%
 
ValueCountFrequency (%) 
0802980.3%
 
13< 0.1%
 
23< 0.1%
 
701< 0.1%
 
851< 0.1%
 
ValueCountFrequency (%) 
1996361< 0.1%
 
20241< 0.1%
 
20232< 0.1%
 
2022370.4%
 
20213994.0%
 

year_of_renovation
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0658
Minimum0
Maximum2021
Zeros9960
Zeros (%)99.6%
Memory size78.1 KiB
2021-05-25T19:14:26.132302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2021
Range2021
Interquartile range (IQR)0

Descriptive statistics

Standard deviation127.2828864
Coefficient of variation (CV)15.78056564
Kurtosis245.1325521
Mean8.0658
Median Absolute Deviation (MAD)0
Skewness15.71884779
Sum80658
Variance16200.93316
MonotocityNot monotonic
2021-05-25T19:14:26.288224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
0996099.6%
 
2019110.1%
 
201860.1%
 
202150.1%
 
20154< 0.1%
 
20174< 0.1%
 
20103< 0.1%
 
20202< 0.1%
 
20051< 0.1%
 
20091< 0.1%
 
Other values (3)3< 0.1%
 
ValueCountFrequency (%) 
0996099.6%
 
20001< 0.1%
 
20051< 0.1%
 
20081< 0.1%
 
20091< 0.1%
 
ValueCountFrequency (%) 
202150.1%
 
20202< 0.1%
 
2019110.1%
 
201860.1%
 
20174< 0.1%
 

closed_price
Real number (ℝ≥0)

MISSING

Distinct246
Distinct (%)24.9%
Missing9014
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean76884.52333
Minimum150
Maximum870989
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2021-05-25T19:14:26.476254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile34000
Q150625
median65000
Q387000
95-th percentile145000
Maximum870989
Range870839
Interquartile range (IQR)36375

Descriptive statistics

Standard deviation55302.67813
Coefficient of variation (CV)0.7192953242
Kurtosis57.2081481
Mean76884.52333
Median Absolute Deviation (MAD)17500
Skewness5.704295165
Sum75808140
Variance3058386209
MonotocityNot monotonic
2021-05-25T19:14:26.678406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60000280.3%
 
65000260.3%
 
70000250.2%
 
45000230.2%
 
53000190.2%
 
67000190.2%
 
55000170.2%
 
100000160.2%
 
40000160.2%
 
56000160.2%
 
Other values (236)7817.8%
 
(Missing)901490.1%
 
ValueCountFrequency (%) 
1501< 0.1%
 
2301< 0.1%
 
2402< 0.1%
 
2501< 0.1%
 
40060.1%
 
ValueCountFrequency (%) 
8709891< 0.1%
 
5300001< 0.1%
 
4700002< 0.1%
 
4350001< 0.1%
 
4000001< 0.1%
 

Interactions

2021-05-25T19:14:01.627604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:01.760455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:01.893107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.022291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.183900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.313357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.434541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.562352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.701538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.841947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:02.969078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.090944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.218315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.419213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.614718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.757921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:03.894943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.039277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.183822image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.358046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.525110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.665698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.799301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:04.927683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.084241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.233431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.455603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.639539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.783989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:05.932641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:06.069321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:06.206334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:06.364493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:06.783682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:06.935639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.087904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.235130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.380332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.536392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.680679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.812581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:07.951693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.096623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.242027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.411878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.593209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.827106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:08.974403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.124223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.271061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.406863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.531069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.661297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.790877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:09.928709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.067863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.242989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.383139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.522667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.662304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.789293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:10.924367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.062528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.206646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.353038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.498919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.639880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.790679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:11.944057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.100729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.238115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.372393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.512380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.649603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.793495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:12.945430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.092444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.328912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.502776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.649108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.786194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:13.942863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:14.126058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:14.286646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:14.471420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:14.662092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:14.849744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.045505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.292524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.508675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.656464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.783041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:15.920308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:16.051254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:16.210694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:16.657396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:16.791914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:16.963671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:17.139911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:17.351552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2021-05-25T19:14:26.851613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-25T19:14:27.050793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-25T19:14:27.600725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-25T19:14:27.806926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-25T19:14:28.005374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-25T19:14:17.988643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:18.622692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:18.982972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:14:19.309758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

propertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovationclosed_price
0948301ApartmentUsedAvailableAlbaniaTiranaTiranaIsh EkspozitaIsh Ekspozita67525.0424211019750NaN
1948275ApartmentUsedAvailableAlbaniaTiranaTiranaOxhakuOxhaku59000.0848421019850NaN
2948219ApartmentUsedAvailableAlbaniaTiranaTiranaZogu I ZiZogu I Zi81500.0909022020190NaN
3947861ApartmentUsedAvailableAlbaniaTiranaTiranaAstiriAstiri60000.0687911020150NaN
4947596ApartmentUsedAvailableAlbaniaTiranaTiranaInstitut KamëzInstitut Kamëz73450.09611421020150NaN
5947569ApartmentNewAvailableAlbaniaTiranaTiranaRruga e ElbasanitRruga e Elbasanit235000.020421532020160NaN
6947536ApartmentUsedAvailableAlbaniaTiranaTiranaInstitut KamëzInstitut Kamëz44200.0606911020150NaN
7947462ApartmentNewAvailableAlbaniaTiranaTiranaHipotekaHipoteka179000.011812822020180NaN
8946773ApartmentUsedIn evaluationAlbaniaTiranaTiranaTregu ElektrikTregu Elektrik650000.040448633000NaN
9946757ApartmentUsedAvailableAlbaniaTiranaTiranaStadiumi DinamoStadiumi Dinamo176000.010812632119930NaN

Last rows

propertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovationclosed_price
999086583ApartmentUsedSoldAlbaniaTiranaTiranaUnaza e Re VlorëUnaza e Re Vlorë51000.082932000051000.0
999186582ApartmentNewWithdrawnAlbaniaTiranaTiranaUnaza e Re VlorëUnaza e Re Vlorë150000.012027022000NaN
999286581ApartmentNewWithdrawnAlbaniaTiranaTiranaUnaza e Re VlorëUnaza e Re Vlorë150000.011729122000NaN
999386573ApartmentUsedWithdrawnAlbaniaTiranaTiranaNaNNaN34000.0535311000NaN
999486567ApartmentUsedSoldAlbaniaTiranaTiranaRruga e KavajesRruga e Kavajes75000.010002110072000.0
999586547ApartmentNewWithDrawnAlbaniaTiranaTiranaLiqeni i ThatëLiqeni i Thatë79000.011011822000NaN
999686546ApartmentUsedSoldAlbaniaTiranaTiranaRruga e ElbasanitRruga e Elbasanit140000.0115022000140000.0
999786545ApartmentNewWithdrawnAlbaniaTiranaTiranaKodra e DiellitKodra e Diellit Residence170000.0150032000NaN
999886543ApartmentUsedWithdrawnAlbaniaTiranaTiranaKomuna e ParisitKomuna e Parisit70000.070011000NaN
999986536ApartmentUsedWithdrawnAlbaniaTiranaTiranaZogu I ZiZogu I Zi99000.010110922000NaN